Segmentation as Retention and Recognition: the R&R model
Alhama, R. G., & Zuidema, W.
Segmentation as Retention and Recognition: the R&R model. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.
), Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci 2017)
(pp. 1531-1536). Austin, TX: Cognitive Science Society.
We present the Retention and Recognition model (R&R), a probabilistic exemplar model that accounts for segmentation in Artificial Language Learning experiments. We show that R&R provides an excellent fit to human responses in three segmentation experiments with adults (Frank et al., 2010), outperforming existing models. Additionally, we analyze the results of the simulations and propose alternative explanations for the experimental findings.